Remote Sensing Image Registration Based on Modified SIFT and Feature Slope Grouping
Herng-Hua Chang, Guan-Long Wu, Mao-Hsiung Chiang · IEEE Geoscience and Remote Sensing Letters · 2019
In feature-based remote sensing image registration, the scale-invariant feature transform (SIFT) algorithm has been one of the most popular solutions. However, it is still a challenge to possess an appropriate amount of correct matches while eliminating mismatches. In this letter, inspired by SIFT, an accurate and robust feature matching framework based on feature slope grouping (FSG) for remote sensing image registration is proposed. Our FSG-SIFT algorithm consists of four major phases: modified SIFT, feature slope computation, feature point grouping, and outlier removal and transformation. Specifically, the random sample consensus is adopted to refine the matches followed by the affine transform. The proposed remote sensing image registration algorithm has been validated on a wide variety of high-resolution orthoimagery data. Experimental results with multispectral and multitemporal images suggested that this new image registration algorithm well improved the feature matching accuracy with better registration performance over five state-of-the-art methods.